This article presents an overview of the self-reported health status of the population of the European Union Member States (EU-28) in the year 2018 based on Eurostat data. The self-reported health status of the inhabitants of the Slovak Republic has been analyzed in more detail with regard to the availability of individual data of the survey results from the European Statistics of Income and Living Condition (EU-SILC). The aim of the article is to analyse the relationship between social and demographic characteristics and the self-perceived health of the population in the EU-28 countries and their comparison as well as a comparison with the results found in the Slovak Republic. The characteristics gender, age, educational level, income, employment, and place of residence have been considered as the determinants of the self-reported health status. The obtained results of self-reported health status by selected demographics and social indicators in the European Union Member States have been compared in visual form using tables and graphs. For assessment of impact selected socio-economic and demographic characteristics on the self-perceived health by inhabitants in the Slovak Republic has been used the logistic regression model based on data extracted from the EU SILC 2016 cross-sectional component provided by the Statistical Office of the Slovak Republic. The obtained results can provide valuable information for health protection policy in EU countries and especially in the Slovak Republic. It could also be used to compare self-reported health status in the EU countries and the health status established based on the official health data published by European institutions.
Catastrophic events affect various regions of the world with increasing frequency and intensity. The number of catastrophic events and the amount of economic losses is varying in different world regions. Part of these losses is covered by insurance. Catastrophe events in last years are associated with increases in premiums for some lines of business. The article focus on estimating the amount of net premiums that would be needed to cover the total or insured catastrophic losses in different world regions using Bühlmann and Bühlmann-Straub empirical credibility models based on data from Sigma Swiss Re 2010-2016. The empirical credibility models have been developed to estimate insurance premiums for short term insurance contracts using two ingredients: past data from the risk itself and collateral data from other sources considered to be relevant. In this article we deal with application of these models based on the real data about number of catastrophic events and about the total economic and insured catastrophe losses in seven regions of the world in time period 2009-2015. Estimated credible premiums by world regions provide information how much money in the monitored regions will be need to cover total and insured catastrophic losses in next year.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.